Fire losses in selected property classifications of non‐residential, commercial and residential wood buildings. Part 1: hotels/motels and care homes for aged
Bibliographic record
Abstract
Abstract In an attempt to evaluate the adequacy of building code requirements for selected classifications of non‐residential, commercial and residential wood buildings, researchers at Forintek Canada Corp. have examined Canadian and American fire loss statistics and compared fire losses for the selected classifications of wood buildings with those for similar buildings of non‐combustible construction. They have also examined causal factors associated with fires in those structures, extent of flame and smoke spread, ability of sprinkler systems and building construction to minimize fire losses, and outcomes of fire events. Because of the volume of information that was analysed, the results are being reported through three separate papers. This, the first, presents the ‘big picture’ with respect to fire losses in the selected classifications of non‐residential, commercial and residential structures, and discusses in detail fire losses for hotel/motel properties and care homes for the aged. Copyright © 2006 John Wiley & Sons, Ltd.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".